ABSTRACT Objective: To identify corneal epithelial thickness profiles for early keratoconus diagnosis with the aid of Artificial Intelligence. Methods: A total of 496 eyes (306 normal and 190 with keratoconus) were analyzed using optical coherence tomography images. Results: Epithelial segmentation revealed distinct patterns between groups, highlighting early changes in epithelial thickness in keratoconus eyes. The findings reinforce the potential of artificial intelligence in disease screening, enabling a more precise and objective diagnostic approach. Conclusion: Implementing algorithms such as k-means can […]

ABSTRACT Objective: This study presents a comparative analysis of the performance of three artificial intelligence models (ChatGPT, Scholar GPT, and DeepSeek) in solving the CBO 2022 theoretical exams I and II to obtain the Specialist Certification Exam in Ophthalmology. Methods: A total of 46 valid questions from theoretical exam I and 122 from theoretical exam II were analyzed after the exclusion of 7 cancelled questions. The models were set to operate under standardized conditions, and their responses were compared with […]

ABSTRACT Objective: To assess the diagnostic accuracy of a deep learning tool for cataract detection using retinal images from the Ocular Disease Intelligent Recognition (ODIR) public dataset. Methods: The study was conducted using a publicly available dataset, with a web-based Artificial Intelligence tool. This was an observational, cross-sectional study. A total of 230 fundus images were selected from the ODIR dataset, equally divided between 115 cataract cases and 115 non-cataract cases. The images were processed using the Gobvision Artificial Intelligence […]

ABSTRACT Objective: To assess the diagnostic accuracy of ChatGPT in analyzing OCT images. Methods: A total of 300 OCT images, previously interpreted by specialists, were analyzed by ChatGPT under three scenarios: image-only input; input with additional epidemiological data; and input with detailed clinical data. Sensitivity, specificity, and accuracy rates were compared across scenarios using appropriate statistical analyses (p < 0.05). Results: Overall accuracy was 66.33% in the image-only scenario, 82.33% with epidemiological data, and 92.67% with clinical data. Sensitivity for [...]

ABSTRACT Introduction: To obtain fundoscopy images through portable and low-cost equipment using artificial intelligence to assess the presence of DR. Methods: Fundus images of diabetic patients’ eyes were obtained by using a smartphone coupled to a device with a 20D lens. By using artificial intelligence (AI), the presence of DR was classified by a binary algorithm. Results: 97 ocular fundoscopy images were evaluated (45 normal and 52 with DR). Through AI diagnostic accuracy around was 70% to 100% in the […]

ABSTRACT Currently the “pandemic” of diabetes mellitus is noted. The incidence and prevalence of diabetes and diabetic retinopathy, the most common microvascular complications of diabetes, are exponentially growing due to increased life expectancy in many parts of the world. The increasing number of people suffering from diabetic retinopathy not only highlights medical issues, but also an economic burden, representing a medical and social challenge. It is extremely important to identify a disease as soon as possible and successfully treat it. […]

ABSTRACT It is part of the omic sciences to search for an understanding of how the cellular system of organisms works as well as studying their biological changes. As part of the omic sciences, we can highlight the genomics whose function is the study of genes, the transcriptomics that studies the changes in the transcripts, the proteomics responsible for understanding the changes that occur in proteins, and the metabolomics that studies all the metabolic changes that occur in a certain […]